Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations2,000
Missing cells471
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory343.9 KiB
Average record size in memory176.1 B

Variable types

Text1
Numeric8
Categorical7
Boolean5
DateTime1

Alerts

area_sqm is highly overall correlated with bedrooms and 1 other fieldsHigh correlation
bathrooms is highly overall correlated with bedroomsHigh correlation
bedrooms is highly overall correlated with area_sqm and 2 other fieldsHigh correlation
compound_name is highly overall correlated with districtHigh correlation
district is highly overall correlated with compound_nameHigh correlation
price_egp is highly overall correlated with area_sqm and 1 other fieldsHigh correlation
compound_name has 471 (23.5%) missing valuesMissing
listing_id has unique valuesUnique
building_age_years has 78 (3.9%) zerosZeros

Reproduction

Analysis started2025-11-11 17:33:54.914747
Analysis finished2025-11-11 17:34:00.914843
Duration6 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

listing_id
Text

Unique 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
2025-11-11T19:34:01.013511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters28,000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2,000 ?
Unique (%)100.0%

Sample

1st rowNCR-2024-00001
2nd rowNCR-2024-00002
3rd rowNCR-2024-00003
4th rowNCR-2024-00004
5th rowNCR-2024-00005
ValueCountFrequency (%)
ncr-2024-019851
 
< 0.1%
ncr-2024-019861
 
< 0.1%
ncr-2024-019871
 
< 0.1%
ncr-2024-019881
 
< 0.1%
ncr-2024-019891
 
< 0.1%
ncr-2024-019901
 
< 0.1%
ncr-2024-019911
 
< 0.1%
ncr-2024-019921
 
< 0.1%
ncr-2024-019931
 
< 0.1%
ncr-2024-019941
 
< 0.1%
Other values (1990)1990
99.5%
2025-11-11T19:34:01.181064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
05599
20.0%
24601
16.4%
-4000
14.3%
42600
9.3%
C2000
 
7.1%
N2000
 
7.1%
R2000
 
7.1%
11600
 
5.7%
9600
 
2.1%
8600
 
2.1%
Other values (4)2400
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)28000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
05599
20.0%
24601
16.4%
-4000
14.3%
42600
9.3%
C2000
 
7.1%
N2000
 
7.1%
R2000
 
7.1%
11600
 
5.7%
9600
 
2.1%
8600
 
2.1%
Other values (4)2400
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)28000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
05599
20.0%
24601
16.4%
-4000
14.3%
42600
9.3%
C2000
 
7.1%
N2000
 
7.1%
R2000
 
7.1%
11600
 
5.7%
9600
 
2.1%
8600
 
2.1%
Other values (4)2400
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)28000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
05599
20.0%
24601
16.4%
-4000
14.3%
42600
9.3%
C2000
 
7.1%
N2000
 
7.1%
R2000
 
7.1%
11600
 
5.7%
9600
 
2.1%
8600
 
2.1%
Other values (4)2400
8.6%

price_egp
Real number (ℝ)

High correlation 

Distinct122
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3845500
Minimum1250000
Maximum8200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-11T19:34:01.244731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1250000
5-th percentile2200000
Q13000000
median3750000
Q34600000
95-th percentile5750000
Maximum8200000
Range6950000
Interquartile range (IQR)1600000

Descriptive statistics

Standard deviation1117343.5
Coefficient of variation (CV)0.2905587
Kurtosis0.13053515
Mean3845500
Median Absolute Deviation (MAD)750000
Skewness0.46212358
Sum7.691 × 109
Variance1.2484565 × 1012
MonotonicityNot monotonic
2025-11-11T19:34:01.323981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
335000043
 
2.1%
440000042
 
2.1%
345000039
 
1.9%
320000039
 
1.9%
380000038
 
1.9%
300000038
 
1.9%
260000037
 
1.8%
340000037
 
1.8%
375000037
 
1.8%
385000037
 
1.8%
Other values (112)1613
80.7%
ValueCountFrequency (%)
12500001
 
0.1%
13500001
 
0.1%
14000004
0.2%
14500002
 
0.1%
15500004
0.2%
16500001
 
0.1%
17000005
0.2%
17500004
0.2%
18000004
0.2%
18500007
0.4%
ValueCountFrequency (%)
82000002
0.1%
80000001
 
0.1%
76000002
0.1%
75000001
 
0.1%
73500001
 
0.1%
73000001
 
0.1%
72500002
0.1%
72000001
 
0.1%
71000004
0.2%
70500001
 
0.1%

area_sqm
Real number (ℝ)

High correlation 

Distinct205
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.721
Minimum60
Maximum299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-11T19:34:01.409432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile91
Q1116
median145
Q3184
95-th percentile217
Maximum299
Range239
Interquartile range (IQR)68

Descriptive statistics

Standard deviation43.785703
Coefficient of variation (CV)0.29050831
Kurtosis0.020600521
Mean150.721
Median Absolute Deviation (MAD)33
Skewness0.52507914
Sum301442
Variance1917.1878
MonotonicityNot monotonic
2025-11-11T19:34:01.490201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14633
 
1.7%
14733
 
1.7%
14033
 
1.7%
14231
 
1.6%
14129
 
1.5%
14827
 
1.4%
14325
 
1.2%
14425
 
1.2%
14523
 
1.1%
9123
 
1.1%
Other values (195)1718
85.9%
ValueCountFrequency (%)
601
 
0.1%
613
 
0.1%
622
 
0.1%
633
 
0.1%
641
 
0.1%
673
 
0.1%
681
 
0.1%
692
 
0.1%
701
 
0.1%
718
0.4%
ValueCountFrequency (%)
2993
0.1%
2972
0.1%
2952
0.1%
2922
0.1%
2911
 
0.1%
2891
 
0.1%
2881
 
0.1%
2871
 
0.1%
2841
 
0.1%
2831
 
0.1%

bedrooms
Categorical

High correlation 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
2
924 
3
896 
4
 
90
1
 
90

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2,000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2924
46.2%
3896
44.8%
490
 
4.5%
190
 
4.5%

Length

2025-11-11T19:34:01.560738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T19:34:01.601326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2924
46.2%
3896
44.8%
490
 
4.5%
190
 
4.5%

Most occurring characters

ValueCountFrequency (%)
2924
46.2%
3896
44.8%
490
 
4.5%
190
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2924
46.2%
3896
44.8%
490
 
4.5%
190
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2924
46.2%
3896
44.8%
490
 
4.5%
190
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2924
46.2%
3896
44.8%
490
 
4.5%
190
 
4.5%

bathrooms
Categorical

High correlation 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
3
797 
2
586 
4
381 
1
207 
5
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2,000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row4
5th row3

Common Values

ValueCountFrequency (%)
3797
39.9%
2586
29.3%
4381
19.1%
1207
 
10.3%
529
 
1.5%

Length

2025-11-11T19:34:01.652400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T19:34:01.698296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3797
39.9%
2586
29.3%
4381
19.1%
1207
 
10.3%
529
 
1.5%

Most occurring characters

ValueCountFrequency (%)
3797
39.9%
2586
29.3%
4381
19.1%
1207
 
10.3%
529
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3797
39.9%
2586
29.3%
4381
19.1%
1207
 
10.3%
529
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3797
39.9%
2586
29.3%
4381
19.1%
1207
 
10.3%
529
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3797
39.9%
2586
29.3%
4381
19.1%
1207
 
10.3%
529
 
1.5%

floor_number
Real number (ℝ)

Distinct15
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.175
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-11T19:34:01.750081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median8
Q312
95-th percentile15
Maximum15
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.2617433
Coefficient of variation (CV)0.52131417
Kurtosis-1.1902351
Mean8.175
Median Absolute Deviation (MAD)4
Skewness-0.041466921
Sum16350
Variance18.162456
MonotonicityNot monotonic
2025-11-11T19:34:01.803734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
12162
 
8.1%
5153
 
7.6%
8147
 
7.3%
13143
 
7.1%
6141
 
7.0%
15137
 
6.9%
3132
 
6.6%
10131
 
6.6%
11131
 
6.6%
14126
 
6.3%
Other values (5)597
29.8%
ValueCountFrequency (%)
1122
6.1%
2106
5.3%
3132
6.6%
4123
6.2%
5153
7.6%
6141
7.0%
7123
6.2%
8147
7.3%
9123
6.2%
10131
6.6%
ValueCountFrequency (%)
15137
6.9%
14126
6.3%
13143
7.1%
12162
8.1%
11131
6.6%
10131
6.6%
9123
6.2%
8147
7.3%
7123
6.2%
6141
7.0%

building_age_years
Real number (ℝ)

Zeros 

Distinct21
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.3175
Minimum0
Maximum20
Zeros78
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-11T19:34:01.864479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q315
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0342273
Coefficient of variation (CV)0.58485363
Kurtosis-1.2086138
Mean10.3175
Median Absolute Deviation (MAD)5
Skewness-0.054189325
Sum20635
Variance36.4119
MonotonicityNot monotonic
2025-11-11T19:34:01.923701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
15112
 
5.6%
6110
 
5.5%
12109
 
5.5%
13107
 
5.3%
20106
 
5.3%
19105
 
5.2%
7100
 
5.0%
8100
 
5.0%
1499
 
5.0%
1699
 
5.0%
Other values (11)953
47.6%
ValueCountFrequency (%)
078
3.9%
197
4.9%
279
4.0%
392
4.6%
489
4.5%
593
4.7%
6110
5.5%
7100
5.0%
8100
5.0%
981
4.0%
ValueCountFrequency (%)
20106
5.3%
19105
5.2%
1890
4.5%
1798
4.9%
1699
5.0%
15112
5.6%
1499
5.0%
13107
5.3%
12109
5.5%
1184
4.2%

district
Categorical

High correlation 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
Fifth Settlement
700 
Rehab City
500 
Katameya
400 
Madinaty
300 
New Cairo (Other)
100 

Length

Max length17
Median length16
Mean length11.75
Min length8

Characters and Unicode

Total characters23,500
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMadinaty
2nd rowFifth Settlement
3rd rowRehab City
4th rowKatameya
5th rowRehab City

Common Values

ValueCountFrequency (%)
Fifth Settlement700
35.0%
Rehab City500
25.0%
Katameya400
20.0%
Madinaty300
15.0%
New Cairo (Other)100
 
5.0%

Length

2025-11-11T19:34:02.006654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T19:34:02.050488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fifth700
20.6%
settlement700
20.6%
rehab500
14.7%
city500
14.7%
katameya400
11.8%
madinaty300
8.8%
new100
 
2.9%
cairo100
 
2.9%
other100
 
2.9%

Most occurring characters

ValueCountFrequency (%)
t4100
17.4%
e3200
13.6%
a2400
10.2%
i1600
 
6.8%
1400
 
6.0%
h1300
 
5.5%
y1200
 
5.1%
m1100
 
4.7%
n1000
 
4.3%
F700
 
3.0%
Other values (16)5500
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)23500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t4100
17.4%
e3200
13.6%
a2400
10.2%
i1600
 
6.8%
1400
 
6.0%
h1300
 
5.5%
y1200
 
5.1%
m1100
 
4.7%
n1000
 
4.3%
F700
 
3.0%
Other values (16)5500
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)23500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t4100
17.4%
e3200
13.6%
a2400
10.2%
i1600
 
6.8%
1400
 
6.0%
h1300
 
5.5%
y1200
 
5.1%
m1100
 
4.7%
n1000
 
4.3%
F700
 
3.0%
Other values (16)5500
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)23500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t4100
17.4%
e3200
13.6%
a2400
10.2%
i1600
 
6.8%
1400
 
6.0%
h1300
 
5.5%
y1200
 
5.1%
m1100
 
4.7%
n1000
 
4.3%
F700
 
3.0%
Other values (16)5500
23.4%

compound_name
Categorical

High correlation  Missing 

Distinct18
Distinct (%)1.2%
Missing471
Missing (%)23.5%
Memory size15.8 KiB
Mountain View
146 
Hyde Park
136 
Palm Hills
135 
Moon Valley
134 
Rehab 3
114 
Other values (13)
864 

Length

Max length16
Median length14
Mean length10.389797
Min length7

Characters and Unicode

Total characters15,886
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLake View
2nd rowRehab 3
3rd rowRehab 4
4th rowKatameya Plaza
5th rowMadinaty B2

Common Values

ValueCountFrequency (%)
Mountain View146
 
7.3%
Hyde Park136
 
6.8%
Palm Hills135
 
6.8%
Moon Valley134
 
6.7%
Rehab 3114
 
5.7%
Rehab 297
 
4.9%
Rehab 196
 
4.8%
Rehab 490
 
4.5%
Katameya Heights82
 
4.1%
Lake View80
 
4.0%
Other values (8)419
20.9%
(Missing)471
23.5%

Length

2025-11-11T19:34:02.116919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rehab397
 
13.0%
madinaty233
 
7.6%
katameya227
 
7.4%
view226
 
7.4%
mountain146
 
4.8%
hyde136
 
4.4%
park136
 
4.4%
hills135
 
4.4%
palm135
 
4.4%
moon134
 
4.4%
Other values (17)1153
37.7%

Most occurring characters

ValueCountFrequency (%)
a2381
15.0%
1529
 
9.6%
e1394
 
8.8%
i904
 
5.7%
n764
 
4.8%
y754
 
4.7%
l747
 
4.7%
t712
 
4.5%
M537
 
3.4%
h479
 
3.0%
Other values (27)5685
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)15886
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a2381
15.0%
1529
 
9.6%
e1394
 
8.8%
i904
 
5.7%
n764
 
4.8%
y754
 
4.7%
l747
 
4.7%
t712
 
4.5%
M537
 
3.4%
h479
 
3.0%
Other values (27)5685
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15886
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a2381
15.0%
1529
 
9.6%
e1394
 
8.8%
i904
 
5.7%
n764
 
4.8%
y754
 
4.7%
l747
 
4.7%
t712
 
4.5%
M537
 
3.4%
h479
 
3.0%
Other values (27)5685
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15886
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a2381
15.0%
1529
 
9.6%
e1394
 
8.8%
i904
 
5.7%
n764
 
4.8%
y754
 
4.7%
l747
 
4.7%
t712
 
4.5%
M537
 
3.4%
h479
 
3.0%
Other values (27)5685
35.8%

distance_to_auc_km
Real number (ℝ)

Distinct231
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.4911
Minimum2
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-11T19:34:02.183257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.2
Q17.7
median13.2
Q319.4
95-th percentile23.8
Maximum25
Range23
Interquartile range (IQR)11.7

Descriptive statistics

Standard deviation6.6885371
Coefficient of variation (CV)0.49577404
Kurtosis-1.2272539
Mean13.4911
Median Absolute Deviation (MAD)5.9
Skewness0.014661098
Sum26982.2
Variance44.736529
MonotonicityNot monotonic
2025-11-11T19:34:02.265857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.118
 
0.9%
17.617
 
0.9%
8.317
 
0.9%
19.417
 
0.9%
4.316
 
0.8%
9.215
 
0.8%
20.215
 
0.8%
23.315
 
0.8%
21.215
 
0.8%
714
 
0.7%
Other values (221)1841
92.0%
ValueCountFrequency (%)
22
 
0.1%
2.111
0.5%
2.26
0.3%
2.310
0.5%
2.48
0.4%
2.510
0.5%
2.68
0.4%
2.78
0.4%
2.87
0.4%
2.99
0.4%
ValueCountFrequency (%)
257
0.4%
24.95
 
0.2%
24.814
0.7%
24.713
0.7%
24.67
0.4%
24.510
0.5%
24.45
 
0.2%
24.39
0.4%
24.23
 
0.1%
24.15
 
0.2%

distance_to_mall_km
Real number (ℝ)

Distinct76
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2587
Minimum0.5
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-11T19:34:02.345886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.8
Q12.3
median4.2
Q36.2
95-th percentile7.7
Maximum8
Range7.5
Interquartile range (IQR)3.9

Descriptive statistics

Standard deviation2.1998759
Coefficient of variation (CV)0.51656043
Kurtosis-1.2186124
Mean4.2587
Median Absolute Deviation (MAD)2
Skewness0.02252415
Sum8517.4
Variance4.839454
MonotonicityNot monotonic
2025-11-11T19:34:02.423406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.951
 
2.5%
7.238
 
1.9%
7.737
 
1.8%
4.435
 
1.8%
0.835
 
1.8%
1.834
 
1.7%
3.733
 
1.7%
7.933
 
1.7%
1.233
 
1.7%
6.333
 
1.7%
Other values (66)1638
81.9%
ValueCountFrequency (%)
0.512
 
0.6%
0.632
1.6%
0.724
1.2%
0.835
1.8%
0.918
0.9%
129
1.5%
1.127
1.4%
1.233
1.7%
1.324
1.2%
1.423
1.1%
ValueCountFrequency (%)
815
 
0.8%
7.933
1.7%
7.830
1.5%
7.737
1.8%
7.625
1.2%
7.530
1.5%
7.424
1.2%
7.312
 
0.6%
7.238
1.9%
7.132
1.6%

distance_to_metro_km
Real number (ℝ)

Distinct121
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0025
Minimum3
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-11T19:34:02.505327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.5
Q16
median9.1
Q312
95-th percentile14.3
Maximum15
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4651461
Coefficient of variation (CV)0.38490931
Kurtosis-1.1810264
Mean9.0025
Median Absolute Deviation (MAD)3
Skewness-0.03727291
Sum18005
Variance12.007237
MonotonicityNot monotonic
2025-11-11T19:34:02.586107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.227
 
1.4%
3.926
 
1.3%
8.926
 
1.3%
7.725
 
1.2%
7.124
 
1.2%
14.223
 
1.1%
13.923
 
1.1%
10.723
 
1.1%
3.423
 
1.1%
11.622
 
1.1%
Other values (111)1758
87.9%
ValueCountFrequency (%)
38
 
0.4%
3.117
0.9%
3.215
0.8%
3.322
1.1%
3.423
1.1%
3.522
1.1%
3.620
1.0%
3.720
1.0%
3.816
0.8%
3.926
1.3%
ValueCountFrequency (%)
1513
0.7%
14.917
0.9%
14.813
0.7%
14.710
0.5%
14.614
0.7%
14.512
0.6%
14.412
0.6%
14.317
0.9%
14.223
1.1%
14.114
0.7%

finishing_type
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
Lux
796 
Semi-finished
545 
Super Lux
455 
Unfinished
204 

Length

Max length13
Median length10
Mean length7.804
Min length3

Characters and Unicode

Total characters15,608
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLux
2nd rowLux
3rd rowLux
4th rowLux
5th rowLux

Common Values

ValueCountFrequency (%)
Lux796
39.8%
Semi-finished545
27.3%
Super Lux455
22.8%
Unfinished204
 
10.2%

Length

2025-11-11T19:34:02.653259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T19:34:02.694985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
lux1251
51.0%
semi-finished545
22.2%
super455
 
18.5%
unfinished204
 
8.3%

Most occurring characters

ValueCountFrequency (%)
i2043
13.1%
e1749
11.2%
u1706
10.9%
L1251
 
8.0%
x1251
 
8.0%
S1000
 
6.4%
n953
 
6.1%
s749
 
4.8%
f749
 
4.8%
h749
 
4.8%
Other values (7)3408
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)15608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i2043
13.1%
e1749
11.2%
u1706
10.9%
L1251
 
8.0%
x1251
 
8.0%
S1000
 
6.4%
n953
 
6.1%
s749
 
4.8%
f749
 
4.8%
h749
 
4.8%
Other values (7)3408
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i2043
13.1%
e1749
11.2%
u1706
10.9%
L1251
 
8.0%
x1251
 
8.0%
S1000
 
6.4%
n953
 
6.1%
s749
 
4.8%
f749
 
4.8%
h749
 
4.8%
Other values (7)3408
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i2043
13.1%
e1749
11.2%
u1706
10.9%
L1251
 
8.0%
x1251
 
8.0%
S1000
 
6.4%
n953
 
6.1%
s749
 
4.8%
f749
 
4.8%
h749
 
4.8%
Other values (7)3408
21.8%

has_balcony
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
True
1493 
False
507 
ValueCountFrequency (%)
True1493
74.7%
False507
 
25.4%
2025-11-11T19:34:02.739459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

has_parking
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
True
1342 
False
658 
ValueCountFrequency (%)
True1342
67.1%
False658
32.9%
2025-11-11T19:34:02.767744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

has_security
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
True
1618 
False
382 
ValueCountFrequency (%)
True1618
80.9%
False382
 
19.1%
2025-11-11T19:34:02.795530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
True
1206 
False
794 
ValueCountFrequency (%)
True1206
60.3%
False794
39.7%
2025-11-11T19:34:02.825214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

view_type
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
Street
846 
Garden
696 
Compound
427 
Nile
 
31

Length

Max length8
Median length6
Mean length6.396
Min length4

Characters and Unicode

Total characters12,792
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGarden
2nd rowCompound
3rd rowGarden
4th rowStreet
5th rowStreet

Common Values

ValueCountFrequency (%)
Street846
42.3%
Garden696
34.8%
Compound427
21.3%
Nile31
 
1.6%

Length

2025-11-11T19:34:03.354287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T19:34:03.407292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
street846
42.3%
garden696
34.8%
compound427
21.3%
nile31
 
1.6%

Most occurring characters

ValueCountFrequency (%)
e2419
18.9%
t1692
13.2%
r1542
12.1%
n1123
8.8%
d1123
8.8%
o854
 
6.7%
S846
 
6.6%
a696
 
5.4%
G696
 
5.4%
C427
 
3.3%
Other values (6)1374
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)12792
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e2419
18.9%
t1692
13.2%
r1542
12.1%
n1123
8.8%
d1123
8.8%
o854
 
6.7%
S846
 
6.6%
a696
 
5.4%
G696
 
5.4%
C427
 
3.3%
Other values (6)1374
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12792
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e2419
18.9%
t1692
13.2%
r1542
12.1%
n1123
8.8%
d1123
8.8%
o854
 
6.7%
S846
 
6.6%
a696
 
5.4%
G696
 
5.4%
C427
 
3.3%
Other values (6)1374
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12792
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e2419
18.9%
t1692
13.2%
r1542
12.1%
n1123
8.8%
d1123
8.8%
o854
 
6.7%
S846
 
6.6%
a696
 
5.4%
G696
 
5.4%
C427
 
3.3%
Other values (6)1374
10.7%
Distinct180
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
Minimum2025-05-11 00:00:00
Maximum2025-11-06 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-11T19:34:03.468395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:34:03.559539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

days_on_market
Real number (ℝ)

Distinct180
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.43
Minimum1
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-11T19:34:03.650858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q145.75
median88
Q3133
95-th percentile172
Maximum180
Range179
Interquartile range (IQR)87.25

Descriptive statistics

Standard deviation51.960225
Coefficient of variation (CV)0.5810156
Kurtosis-1.1890491
Mean89.43
Median Absolute Deviation (MAD)44
Skewness0.028396632
Sum178860
Variance2699.865
MonotonicityNot monotonic
2025-11-11T19:34:03.729827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1018
 
0.9%
12818
 
0.9%
1318
 
0.9%
618
 
0.9%
10818
 
0.9%
15117
 
0.9%
4617
 
0.9%
6817
 
0.9%
16317
 
0.9%
6217
 
0.9%
Other values (170)1825
91.2%
ValueCountFrequency (%)
113
0.7%
211
0.5%
311
0.5%
413
0.7%
59
0.4%
618
0.9%
711
0.5%
810
0.5%
95
 
0.2%
1018
0.9%
ValueCountFrequency (%)
18013
0.7%
1795
 
0.2%
1789
0.4%
17712
0.6%
17615
0.8%
17514
0.7%
17411
0.5%
17315
0.8%
17211
0.5%
17112
0.6%

seller_type
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
Broker
1313 
Owner
687 

Length

Max length6
Median length6
Mean length5.6565
Min length5

Characters and Unicode

Total characters11,313
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBroker
2nd rowBroker
3rd rowBroker
4th rowBroker
5th rowOwner

Common Values

ValueCountFrequency (%)
Broker1313
65.6%
Owner687
34.4%

Length

2025-11-11T19:34:03.806514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T19:34:03.845571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
broker1313
65.6%
owner687
34.4%

Most occurring characters

ValueCountFrequency (%)
r3313
29.3%
e2000
17.7%
o1313
 
11.6%
B1313
 
11.6%
k1313
 
11.6%
O687
 
6.1%
w687
 
6.1%
n687
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)11313
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r3313
29.3%
e2000
17.7%
o1313
 
11.6%
B1313
 
11.6%
k1313
 
11.6%
O687
 
6.1%
w687
 
6.1%
n687
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11313
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r3313
29.3%
e2000
17.7%
o1313
 
11.6%
B1313
 
11.6%
k1313
 
11.6%
O687
 
6.1%
w687
 
6.1%
n687
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11313
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r3313
29.3%
e2000
17.7%
o1313
 
11.6%
B1313
 
11.6%
k1313
 
11.6%
O687
 
6.1%
w687
 
6.1%
n687
 
6.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
True
1694 
False
306 
ValueCountFrequency (%)
True1694
84.7%
False306
 
15.3%
2025-11-11T19:34:03.875320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2025-11-11T19:34:00.122257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:55.803511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:56.319676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:57.562980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:58.071161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:58.615398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:59.104695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:59.631160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:34:00.186509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:55.867058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:56.381477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:57.624068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:58.141730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:58.676951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:59.170478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:59.689840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:34:00.248145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:55.928382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:56.440400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:57.688656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:58.210686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:58.735321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:59.231866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:59.748974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:34:00.314825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:55.991385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:56.500784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:57.750108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:58.275004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:58.799972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:59.292127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:59.809501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:34:00.382866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:56.061689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:56.573249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:57.815711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:58.343240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:58.866857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:59.366601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:59.874818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:34:00.442011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:56.123370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:57.382702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:57.876878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:58.408961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:58.921701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:59.429785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:59.938863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:34:00.527075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:56.192048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:57.442917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:57.940351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:58.478859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:58.984451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:59.497424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:34:00.000458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:34:00.601072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:56.253824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:57.502374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:58.002784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:58.546489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:59.043721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:33:59.565113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:34:00.060634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-11T19:34:03.924311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
area_sqmbathroomsbedroomsbuilding_age_yearscompound_namedays_on_marketdistance_to_auc_kmdistance_to_mall_kmdistance_to_metro_kmdistrictfinishing_typefloor_numberhas_amenitieshas_balconyhas_parkinghas_securityis_negotiableprice_egpseller_typeview_type
area_sqm1.0000.3820.820-0.0050.000-0.0430.0030.020-0.0080.0000.0000.0330.0000.0370.0670.0360.0740.8510.0000.035
bathrooms0.3821.0000.5180.0370.0650.0290.0210.0000.0350.0090.0370.0000.0000.0000.0000.0410.0050.3320.0000.042
bedrooms0.8200.5181.0000.0590.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.0540.0390.0220.6190.0430.021
building_age_years-0.0050.0370.0591.0000.000-0.001-0.005-0.0300.0040.0040.0550.0300.0410.0380.0000.0000.048-0.1460.0590.044
compound_name0.0000.0650.0000.0001.0000.0000.0000.0000.0000.9960.0090.0410.0000.0000.0000.0720.0730.0000.0000.033
days_on_market-0.0430.0290.032-0.0010.0001.0000.006-0.0110.0290.0000.0130.0050.0390.0000.0000.0000.061-0.0290.0000.017
distance_to_auc_km0.0030.0210.000-0.0050.0000.0061.0000.030-0.0130.0000.0170.0070.0000.0480.0560.0450.000-0.0400.0580.000
distance_to_mall_km0.0200.0000.000-0.0300.000-0.0110.0301.000-0.0350.0300.028-0.0050.0570.0480.0000.0000.000-0.0080.0000.000
distance_to_metro_km-0.0080.0350.0000.0040.0000.029-0.013-0.0351.0000.0000.0000.0300.0000.0000.0330.0000.0350.0140.0270.011
district0.0000.0090.0000.0040.9960.0000.0000.0300.0001.0000.0000.0290.0000.0000.0000.0000.0700.0000.0000.000
finishing_type0.0000.0370.0000.0550.0090.0130.0170.0280.0000.0001.0000.0000.0000.0240.0300.0000.0000.1650.0000.000
floor_number0.0330.0000.0000.0300.0410.0050.007-0.0050.0300.0290.0001.0000.0000.0000.0440.0000.000-0.0070.0430.000
has_amenities0.0000.0000.0000.0410.0000.0390.0000.0570.0000.0000.0000.0001.0000.0000.0120.0170.0000.1380.0000.000
has_balcony0.0370.0000.0000.0380.0000.0000.0480.0480.0000.0000.0240.0000.0001.0000.0000.0000.0000.0000.0000.013
has_parking0.0670.0000.0540.0000.0000.0000.0560.0000.0330.0000.0300.0440.0120.0001.0000.0000.0060.0200.0000.000
has_security0.0360.0410.0390.0000.0720.0000.0450.0000.0000.0000.0000.0000.0170.0000.0001.0000.0170.0000.0000.000
is_negotiable0.0740.0050.0220.0480.0730.0610.0000.0000.0350.0700.0000.0000.0000.0000.0060.0171.0000.0360.0000.030
price_egp0.8510.3320.619-0.1460.000-0.029-0.040-0.0080.0140.0000.165-0.0070.1380.0000.0200.0000.0361.0000.0000.044
seller_type0.0000.0000.0430.0590.0000.0000.0580.0000.0270.0000.0000.0430.0000.0000.0000.0000.0000.0001.0000.000
view_type0.0350.0420.0210.0440.0330.0170.0000.0000.0110.0000.0000.0000.0000.0130.0000.0000.0300.0440.0001.000

Missing values

2025-11-11T19:34:00.716178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-11T19:34:00.839773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

listing_idprice_egparea_sqmbedroomsbathroomsfloor_numberbuilding_age_yearsdistrictcompound_namedistance_to_auc_kmdistance_to_mall_kmdistance_to_metro_kmfinishing_typehas_balconyhas_parkinghas_securityhas_amenitiesview_typelisting_datedays_on_marketseller_typeis_negotiable
0NCR-2024-000013650000145321218MadinatyNaN15.92.113.1LuxYesYesYesNoGarden2025-08-2376BrokerYes
1NCR-2024-000023900000155331517Fifth SettlementNaN23.83.06.4LuxYesYesYesYesCompound2025-08-1287BrokerYes
2NCR-2024-00003265000010923514Rehab CityNaN9.87.79.9LuxYesYesYesYesGarden2025-09-2048BrokerYes
3NCR-2024-00004545000021934101KatameyaLake View4.54.44.5LuxYesYesYesYesStreet2025-09-1058BrokerYes
4NCR-2024-0000524500009623413Rehab CityRehab 313.63.511.3LuxYesNoYesNoStreet2025-09-1157OwnerYes
5NCR-2024-00006300000013023220Rehab CityRehab 44.04.411.8LuxYesYesYesYesCompound2025-08-2772OwnerYes
6NCR-2024-0000731000001032349KatameyaKatameya Plaza24.36.33.5LuxYesYesYesYesCompound2025-07-21109OwnerYes
7NCR-2024-000085550000242441215MadinatyMadinaty B22.93.111.1Semi-finishedYesNoYesYesGarden2025-07-14116BrokerNo
8NCR-2024-0000940000001653486Fifth SettlementHyde Park19.31.88.0LuxNoNoYesNoStreet2025-07-28102OwnerYes
9NCR-2024-0001067500002173457Rehab CityRehab 114.14.94.5Super LuxYesNoNoYesGarden2025-08-0693BrokerYes
listing_idprice_egparea_sqmbedroomsbathroomsfloor_numberbuilding_age_yearsdistrictcompound_namedistance_to_auc_kmdistance_to_mall_kmdistance_to_metro_kmfinishing_typehas_balconyhas_parkinghas_securityhas_amenitiesview_typelisting_datedays_on_marketseller_typeis_negotiable
1990NCR-2024-0199141500001583314Fifth SettlementMoon Valley13.93.57.5Semi-finishedYesYesYesYesGarden2025-07-24106BrokerYes
1991NCR-2024-019924650000166331010MadinatyMadinaty B17.26.813.8UnfinishedYesNoNoYesGarden2025-06-01159BrokerYes
1992NCR-2024-01993390000014033117KatameyaKatameya Plaza11.46.03.2Super LuxYesYesNoYesStreet2025-06-16144BrokerYes
1993NCR-2024-0199449000001723274MadinatyMadinaty B422.26.111.4Super LuxYesYesYesYesStreet2025-07-06124BrokerYes
1994NCR-2024-01995360000012923310KatameyaKatameya Heights14.12.014.5LuxNoYesYesNoStreet2025-09-0167BrokerYes
1995NCR-2024-0199636000001212394Rehab CityNaN9.74.011.5LuxNoYesYesYesStreet2025-09-2741BrokerYes
1996NCR-2024-0199723000009423616Rehab CityRehab 16.87.014.9Semi-finishedYesNoYesYesGarden2025-09-2246OwnerYes
1997NCR-2024-0199841000001563253KatameyaKatameya Plaza20.44.311.3Super LuxYesYesNoNoGarden2025-08-2178OwnerYes
1998NCR-2024-0199920500009723814Rehab CityNaN16.45.63.4UnfinishedYesYesYesNoGarden2025-06-20140BrokerYes
1999NCR-2024-02000540000019634818Rehab CityRehab 112.84.912.9LuxNoYesYesYesStreet2025-10-0929BrokerYes